Stefano Allesina of the University of Chicago and Mercedes Pascual of the University of Michigan began with a simple hunch. Google’s search engine uses an algorithm called PageRank to identify the most important Web sites on a given topic by analyzing links: a Web page is important if other important pages link to it. How different is this, really, Allesina and Pascual wondered, from an ecosystem, in which a species is important if other important species eat it?

Allesina and Pascual borrowed Google’s PageRank algorithm and modified it to model ecosystems in the natural world. As they explained in September in the journal PLoS Computational Biology, the modified algorithm was more efficient than existing ecosystem-extinction models at identifying which species’ extinction would cause the greatest number of other species in the food web also to go extinct. “Our algorithm is faster and computationally simpler,” Allesina says.

The PageRank algorithm could be useful in analyzing other networks too. The world features countless interconnected systems ranging in size from the minuscule (metabolic networks within a single cell) to the immense (international financial markets). After publishing the paper, Allesina received e-mail messages from dozens of researchers interested in adapting the PageRank algorithm. “PageRank is a technique for finding hidden flows in huge quantities of data,” says Yonatan Zunger, a Google software engineer who used to work on search technology and who contacted Allesina after seeing his research. “There are all kinds of networks. PageRank is enormously applicable beyond the Web.”